Identifying Faults of Rolling Element Based on Persistence Spectrum and Convolutional Neural Network With ResNet Structure

Lee, Chun‐Yao; Le, Truong-An

doi:10.1109/access.2021.3083646

Cited by 18 publications

(6 citation statements)

References 32 publications

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“…In addition, expert knowledge is required for fault identification. The Persistence Spectrum may be a potential solution that we suggest to overcome the challenges associated with detecting fault related components in inverter-fed modes with a suitable calculation burden [138,139]. It is widely used to detect the hidden weak signals in other powerful signals.…”

Section: Discussion On Signal Processing-based Methods For Bbf Detect...mentioning

confidence: 99%

Broken Bar Fault Detection and Diagnosis Techniques for Induction Motors and Drives: State of the Art

2022

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Motors are the higher energy-conversion devices that consume around 40% of the global electrical generated energy. Induction motors are the most popular motor type due to their reliability, robustness, and low cost. Therefore, both condition monitoring and fault diagnosis of induction motor faults have motivated considerable research efforts. In this paper, a comprehensive review of the recent techniques proposed in the literature for broken bar faults detection and diagnosis is presented. This paper mainly investigates the fault detection methods in line-fed and inverter-fed motors proposed after 2015 and published in most relevant journals and conferences. The introduced review has deeply discussed the main features of the reported methods and compared them in many different aspects. Finally, the study has highlighted the main issues and the gaps that require more attention from researchers in this field.

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Section: Discussion On Signal Processing-based Methods For Bbf Detect...mentioning

confidence: 99%

Broken Bar Fault Detection and Diagnosis Techniques for Induction Motors and Drives: State of the Art

2022

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“…As it was stated in [ 38 , 39 , 40 ], the m th element of the STFT matrix is given by Equation (6):

where:…”

Section: Methodsmentioning

confidence: 99%

Automatic Classification of Rotor Faults in Soft-Started Induction Motors, Based on Persistence Spectrum and Convolutional Neural Network Applied to Stray-Flux Signals

Biot-Monterde

Navarro-Navarro

Zamudio-Ramírez

et al. 2022

Sensors

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Due to their robustness, versatility and performance, induction motors (IMs) have been widely used in many industrial applications. Despite their characteristics, these machines are not immune to failures. In this sense, breakage of the rotor bars (BRB) is a common fault, which is mainly related to the high currents flowing along those bars during start-up. In order to reduce the stresses that could lead to the appearance of these faults, the use of soft starters is becoming usual. However, these devices introduce additional components in the current and flux signals, affecting the evolution of the fault-related patterns and so making the fault diagnosis process more difficult. This paper proposes a new method to automatically classify the rotor health state in IMs driven by soft starters. The proposed method relies on obtaining the Persistence Spectrum (PS) of the start-up stray-flux signals. To obtain a proper dataset, Data Augmentation Techniques (DAT) are applied, adding Gaussian noise to the original signals. Then, these PS images are used to train a Convolutional Neural Network (CNN), in order to automatically classify the rotor health state, depending on the severity of the fault, namely: healthy motor, one broken bar and two broken bars. This method has been validated by means of a test bench consisting of a 1.1 kW IM driven by four different soft starters coupled to a DC motor. The results confirm the reliability of the proposed method, obtaining a classification rate of 100.00% when analyzing each model separately and 99.89% when all the models are analyzed at a time.

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“…Moreover, because it has a deeper layer's presentation, the ResNet makes it possible to design deeper learning applications that deal with more complicated real-world problems. Furthermore, it has been shown in the literature that this type of deep network facilitates faster convergence than that achieved by a CNN which does not have a skip connection function [27][28][29].…”

Section: The Residual Neural Network (Resnet) For Feature Learningmentioning

confidence: 99%

Deep-Learning and Vibration-Based System for Wear Size Estimation of Railway Switches and Crossings

Najeh

Lundberg

Kerrouche

2021

Sensors

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The switch and crossing (S&C) is one of the most important parts of the railway infrastructure network due to its significant influence on traffic delays and maintenance costs. Two central questions were investigated in this paper: (I) the first question is related to the feasibility of exploring the vibration data for wear size estimation of railway S&C and (II) the second one is how to take advantage of the Artificial Intelligence (AI)-based framework to design an effective early-warning system at early stage of S&C wear development. The aim of the study was to predict the amount of wear in the entire S&C, using medium-range accelerometer sensors. Vibration data were collected, processed, and used for developing accurate data-driven models. Within this study, AI-based methods and signal-processing techniques were applied and tested in a full-scale S&C test rig at Lulea University of Technology to investigate the effectiveness of the proposed method. A real-scale railway wagon bogie was used to study different relevant types of wear on the switchblades, support rail, middle rail, and crossing part. All the sensors were housed inside the point machine as an optimal location for protection of the data acquisition system from harsh weather conditions such as ice and snow and from the ballast. The vibration data resulting from the measurements were used to feed two different deep-learning architectures, to make it possible to achieve an acceptable correlation between the measured vibration data and the actual amount of wear. The first model is based on the ResNet architecture where the input data are converted to spectrograms. The second model was based on a long short-term memory (LSTM) architecture. The proposed model was tested in terms of its accuracy in wear severity classification. The results show that this machine learning method accurately estimates the amount of wear in different locations in the S&C.

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Identifying Faults of Rolling Element Based on Persistence Spectrum and Convolutional Neural Network With ResNet Structure

Cited by 18 publications

References 32 publications

Broken Bar Fault Detection and Diagnosis Techniques for Induction Motors and Drives: State of the Art

Broken Bar Fault Detection and Diagnosis Techniques for Induction Motors and Drives: State of the Art

Automatic Classification of Rotor Faults in Soft-Started Induction Motors, Based on Persistence Spectrum and Convolutional Neural Network Applied to Stray-Flux Signals

Deep-Learning and Vibration-Based System for Wear Size Estimation of Railway Switches and Crossings

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